List of ML Systems from the market

Machine learning (ML) systems are a type of artificial intelligence (AI) that enable computers to learn from data and improve their performance on a specific task over time. ML systems are used in a wide range of applications, from image recognition and natural language processing to fraud detection and recommendation systems.

Machine Learning Systems _

ML systems can be divided into several categories, including supervised learning, unsupervised learning, and reinforcement learning.

In supervised learning, the system is trained on labeled data, where the correct output is known for each input. In unsupervised learning, the system is trained on unlabeled data, where the goal is to identify patterns and relationships in the data. In reinforcement learning, the system learns through trial and error, where it receives feedback in the form of rewards or penalties based on its actions.

ML systems require large amounts of data to train effectively, and the quality and quantity of the data can have a significant impact on the performance of the system. ML systems also require careful design and tuning to ensure that they are accurate, reliable, and scalable.

Despite these challenges, ML systems have the potential to revolutionize many industries and improve our lives in countless ways. As the field of ML continues to evolve, we can expect to see even more powerful and sophisticated systems that can tackle increasingly complex tasks and challenges.

If you are looking for ideas to learn machine learning, here are some market examples.

Code

import pandas as pd
import plotly.express as px


from IPython.display import display

pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)


df = pd.read_csv('dataset/ml-system.csv')
tag_counts = df['Tag'].value_counts().head(10)
fig = px.bar(tag_counts, orientation='h')

fig.update_layout(
    paper_bgcolor='rgba(0,0,0,0)', 
    plot_bgcolor='rgba(0,0,0,0)',
    images=[dict(
        xref="paper", yref="paper",
        x=0.5, y=0.5,
        sizex=1, sizey=1,
        opacity=0.5,
        layer="below")],
    font=dict(
        color="#6f6feb",
    ),
    margin=dict(
        l=5,
        r=5,
        b=5,
        t=5,
        pad=4
    )
)

fig.show()

df_short = df.drop(['Industry', 'Year', 'Short Description (< 5 words)', 'Link'], axis=1)

display(df_short.head(10))

	Company	Title	Tag
0	Stripe	How we built it: Stripe Radar	fraud detection
1	Walmart	Personalized ‘Complete the Look’ model	recommender system,product feature,CV
2	Uber	Demand and ETR Forecasting at Airports	demand forecasting
3	Pinterest	An ML based approach to proactive advertiser c...	churn prediction
4	Stitch Fix	A New Era of Creativity: Expert-in-the-loop Ge...	product feature,NLP,generative AI
5	Swiggy	Building a mind reader at Swiggy using Data Sc...	product feature,recommender system
6	Microsoft	Large-language models for automatic cloud inci...	ops,generative AI,product feature
7	Foodpanda	Menu Ranking	content personalization
8	Zillow	Building the Neural Zestimate	pricing
9	Airbnb	Prioritizing Home Attributes Based on Guest In...	NLP,item classification,search

Some Definitions

ETA prediction model is a machine learning model that predicts the estimated time of arrival for a given task or process. It is a type of regression model that uses historical data to learn patterns and make predictions about future events.

Product feature is designed to identify and extract product features from textual data such as product descriptions, reviews, and specifications. These systems use natural language processing (NLP) techniques to analyze the text and identify the specific features that are being discussed.

Content personalization is designed to personalize product content for individual users based on their preferences, behavior, and other relevant data. These systems use machine learning algorithms to analyze user data and generate personalized product content that is tailored to the user’s needs and preferences.

Item classification is designed to classify items into different categories or classes based on their features or attributes. These systems use machine learning algorithms to analyze the features of the items and identify patterns that are associated with specific classes.

Demand forecasting is designed to predict the future demand for a product or service based on historical data and other relevant factors. These systems use machine learning algorithms to analyze patterns and trends in the data and generate forecasts of future demand.

References

• https://airtable.com/shrZeywfF21lfSjci/tblTCefZr4laRvmqI

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